Kernel Entropy Component Analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems

In this paper, the authors present a novel Gabor wavelet based Kernel Entropy Component Analysis (KECA) method by integrating the Gabor wavelet transformation (GWT) of facial images with the KECA method for enhanced face recognition performance. Firstly, from the Gabor wavelet transformed images the most important discriminative desirable facial features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations due to illumination and facial expression changes were derived. After that KECA, relating to the Renyi entropy is extended to include cosine kernel function. The KECA with the cosine kernels is then applied on the extracted most important discriminating feature vectors of facial images to obtain only those real kernel ECA eigenvectors that are associated with eigenvalues having positive entropy contribution. Finally, these real KECA features are used for image classification using the L1, L2 distance measures; the Mahalanobis distance measure and the cosine similarity measure. The feasibility of the Gabor based KECA method with the cosine kernel has been successfully tested on both frontal and pose-angled face recognition, using datasets from the ORL, FRAV2D, and the FERET database.

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Pose invariant face recognition for video surveillance system using kernel principle component analysis

10.1117/12.956494 ◽

2012 ◽

Cited By ~ 1

Author(s):

Sepehr Damavandinejadmonfared ◽

Waled Hussein Al-Arashi ◽

Shahrel Azmin Suandi

Keyword(s):

Face Recognition ◽

Video Surveillance ◽

Principle Component Analysis ◽

Surveillance System ◽

Component Analysis ◽

Video Surveillance System ◽

Principle Component

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Real time face recognition of video surveillance system using haar cascade classifier

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v21.i3.pp1389-1399 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1389

Author(s):

Adlan Hakim Ahmad ◽

Sharifah Saon ◽

Abd Kadir Mahamad ◽

Cahyo Darujati ◽

Sri Wiwoho Mudjanarko ◽

...

Keyword(s):

Face Recognition ◽

Real Time ◽

Video Surveillance ◽

Surveillance System ◽

Raspberry Pi ◽

Video Surveillance System ◽

Cascade Classifier ◽

Camera System ◽

Surveillance Camera ◽

Haar Cascade

<div>This project investigates the use of face recognition for a surveillance system. The normal video surveillance system uses in closed-circuit television (CCTV) to record video for security purpose. It is used to identify the identity of a person through their appearances on the recorded video, manually. Today’s video surveillance camera system usually not occupied with a face recognition system. With some modification, a surveillance camera system can be used as face detection and recognition that can be done in real-time. The proposed system makes use of surveillance camera system that can identify the identity of a person automatically by using face recognition of Haar cascade classifier. The hardware used for this project were Raspberry Pi as a processor and Pi Camera as a camera module. The development of this project consist of three main phases which were data gathering, training recognizer, and face recognition process. All three phases have been executed using Python programming and OpenCV library, which have been performed in a Raspbian operation system. From the result, the proposed system successfully displays the output result of human face recognition, with facial angle within ±40°, in medium and normal light condition, and within a distance of 0.4 to 1.2 meter. Targeted image are allowed to wear face accessory as long as not covering the face structure. In conclusion, this system considered, can reduce the cost of manpower in order to identify the identity of a person in real time situation.</div>

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Germany ∙ Video Surveillance and Face Recognition: Current Developments

European Data Protection Law Review ◽

10.21552/edpl/2019/4/14 ◽

2019 ◽

Vol 5 (4) ◽

pp. 544-547

Author(s):

T. Raab

Keyword(s):

Face Recognition ◽

Video Surveillance

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Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030177 ◽

2021 ◽

Vol 10 (3) ◽

pp. 177

Author(s):

Haochen Zou ◽

Keyan Cao ◽

Chong Jiang

Keyword(s):

Video Surveillance ◽

Traffic Management ◽

Visual Analysis ◽

Urban Traffic ◽

License Plate ◽

Road Vehicle ◽

Urban Road ◽

Surveillance Camera ◽

Vehicle Density ◽

Spatio Temporal

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.

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